Browsing by Author "Arsan,T."

Now showing 1 - 13 of 13

Citation Count: 0
Advancing Anomaly Detection in Time Series Data: A Knowledge Distillation Approach with LSTM Model
(Institute of Electrical and Electronics Engineers Inc., 2023) Arsan, Taner; Camlidere,B.; Yildiz,E.; Guler,A.K.; Alsan,H.F.; Arsan,T.
This paper focuses on enhancing anomaly detection in time series data using deep learning techniques. Particularly, it investigates the integration of knowledge distillation with LSTM-based models for improved precision, efficiency, and interpretability. The study outlines objectives such as dataset preprocessing, developing a novel LSTM-knowledge distillation framework, incorporating Grafana, InfluxDB, Flask API with Docker, performance assessment, and practical implications. Results highlight the efficacy of knowledge distillation in enhancing student model performance. The proposed approach enhances anomaly detection, offering a viable solution for real-world applications. © 2023 IEEE.
Citation Count: 10
Big data platform development with a domain specific language for telecom industries
(IEEE Computer Society, 2013) Arsan, Taner; Altuntas,S.; Bozkus,Z.; Arsan,T.
This paper introduces a system that offer a special big data analysis platform with Domain Specific Language for telecom industries. This platform has three main parts that suggests a new kind of domain specific system for processing and visualization of large data files for telecom organizations. These parts are Domain Specific Language (DSL), Parallel Processing/Analyzing Platform for Big Data and an Integrated Result Viewer. In addition to these main parts, Distributed File Descriptor (DFD) is designed for passing information between these modules and organizing communication. To find out benefits of this domain specific solution, standard framework of big data concept is examined carefully. Big data concept has special infrastructure and tools to perform for data storing, processing, analyzing operations. This infrastructure can be grouped as four different parts, these are infrastructure, programming models, high performance schema free databases, and processing-analyzing. Although there are lots of advantages of Big Data concept, it is still very difficult to manage these systems for many enterprises. Therefore, this study suggest a new higher level language, called as DSL which helps enterprises to process big data without writing any complex low level traditional parallel processing codes, a new kind of result viewer and this paper also presents a Big Data solution system that is called Petaminer. © 2013 IEEE.
Citation Count: 0
CHAOTIC – DETERMINISTIC OR RANDOM NATURE OF EARTHQUAKES: A PHASE SPACE ANALYSIS
(Symmetrion, 2023) Arsan, Taner; Arsan,T.
Using the phase space approach, time series analysis of high EV1 and low EV2 intense two different earthquakes that occurred at the nearly same precise spot, at different times, and were measured with the same sensor of a broadband station were studied. Time series data of strong, large (EV1) and weak, small (EV2) two earthquake events were analyzed by dividing them into three different regions. Fractal dimensions of the EV1 and EV2 were produced using the box-counting algorithm for east-west (BHE), north-south (BHN), and vertical (BHZ) components. The small, weak earthquake, EV2, created a larger fractal dimension in phase space by implying its random nature in all regions. However, EV1 is a strong, large earthquake that presents deterministic oscillatory behavior at a long-time region. Oscillatory behavior can be named surface wave. EV2 exhibits weak, high-frequency ground oscillations similar to fibrillation before and after the earthquake in the long-term areas. © 2023, Symmetrion. All rights reserved.
Citation Count: 0
Deciphering the Cluster-Specific Marker Genes via Integration of Single Cell RNA Sequencing Datasets
(Institute of Electrical and Electronics Engineers Inc., 2023) Arsan, Taner; Sogunmez,N.; Altaf,A.; Alsan,H.F.; Arsan,T.
Experimental data from brain tissues are critical for tackling the problems in brain development and revealing the underlying mechanisms of disease states. However, obtaining the brain tissue is a major challenge. Human brain organoids hold remarkable promise for this goal, but they suffer from substantial organoid-to-organoid variability. We performed a data-driven analysis on single-cell RNA-sequencing data using 17775 cells isolated from 2 individual organoids. The main goal was to accurately integrate the data coming from unmatched datasets, cluster the cells based on their similarity levels and predict the differentially expressed genes per cell types to reveal novel brain cell types and markers. This research opens a way to map human brain cells and develop novel and precise machine learning algorithms for accurate scRNA-Seq data analysis. © 2023 IEEE.
Citation Count: 0
Enhancing Robotic Performance: Analyzing Force and Torque Measurements for Predicting Execution Failures
(Institute of Electrical and Electronics Engineers Inc., 2023) Arsan, Taner; Alsan,H.F.; Arsan,T.
Robots play an important role in many sectors, automating processes and supplementing human talents. However, guaranteeing reliability is critical for effective integration and widespread adoption. As a result, forecasting and managing these errors is critical. This research examines force and torque measurements in order to better understand the causes and patterns of robot execution errors. We hope to build prediction models that improve robot design and performance, eventually boosting their reliability and efficacy, by using data analysis and machine learning approaches. This study's research aims include using a dataset of force and torque measurements to predict and define robot execution failures, We hope to uncover the complex links between force and torque measurements and failure types, find crucial signals or precursors to failures, and construct strong prediction models for correct failure categorization by tackling these research topics. This study contributes to data science by demonstrating the use of analytics approaches to improve the dependability and performance of robots in real-world scenarios. © 2023 IEEE.
Citation Count: 0
Improving non-line-of-sight situations in indoor positioning with ultra-wideband sensors via federated Kalman filter
(Institute of Advanced Engineering and Science, 2024) Arsan, Taner; Arsan,T.
Ultra-wideband (UWB) technology is renowned for its exceptional performance in fast data transmission and precise positioning. However, it faces sensitivity challenges when the tagged object is not in direct line of sight, resulting in position inaccuracies. Applying the federated Kalman filter (FKF), this research focuses on mitigating position deviation induced by non-line-of-sight (NLOS) scenarios in UWB technology. The utilization of the FKF in NLOS scenarios has demonstrated a noteworthy reduction in position deviation. This study uses the FKF to analyze measurements taken under line-of-sight (LOS) and NLOS conditions within indoor settings. The outcomes of this study provide a promising foundation for future research endeavors in the field of UWB technology, emphasizing the potential for improved performance and accuracy in challenging operational environments. © 2024 Institute of Advanced Engineering and Science. All rights reserved.
Citation Count: 0
Joint Visible Light Communications and Positioning with Dimming;
(Institute of Electrical and Electronics Engineers Inc., 2023) Arsan, Taner; Panayırcı, Erdal; Kumaş,M.B.; Demiryürek,T.; Arsan,T.; Panayirci,E.
In this paper, a novel visible light communication (VLC) and three-dimensional (3D) positioning (VLP) algorithm based on spatial modulation (SM) with dimming capability is proposed. With the help of the signal and pilot symbols observed by a user with two photodetectors (PD) on the receiver, the detection of the transmitted data with the estimated channel and dimming coefficients is performed, and at the same time, the position of the user represented in 3D is determined. The computer simulations conclude that the root mean square (RMS) values of the proposed algorithm in 3D position determination are very low, and the bit error rate (BER) performance is very high. © 2023 IEEE.
Citation Count: 0
Machine Failure Prediction: : A Comparative Anomaly Detection
(Institute of Electrical and Electronics Engineers Inc., 2023) Arsan, Taner; Alsan,H.F.; Arsan,T.
Anomaly detection techniques seek to uncover unusual changes in the expected behavior of target indicators and, when used for intrusion detection, suspect assaults whenever the mentioned deviations are found. This technique is crucial in identifying and flagging abnormal instances in various domains. Several anomaly detection algorithms have been suggested, tested experimentally, and assessed in qualitative and quantitative surveys in the literature. However, there is a scarcity of comparative research, and methodological shortcomings are observed in existing studies. This paper investigates the performance of ten popular anomaly detection models for feature correlation analysis for predictive maintenance to detect machine failure with the most known approaches. The models considered are Local Outlier Factor (LOF), K-Nearest Neighbors (KNN), Support Vector Machines, Elliptic Envelope, Isolation Forest, Decision Tree, Extra Trees, Random Forest, AdaBoost, and Gradient Boosting. We evaluate the models using two scenarios: one with two correlated features and another with all features focused on correlated features. The evaluation metrics used for comparison are assessed by GridSearchCV and RandomizedSearchCV and compared to the cross-validation methods. © 2023 IEEE.
Citation Count: 0
Network Traffic Anomaly Detection Using Quantile Regression with Tolerance
(Institute of Electrical and Electronics Engineers Inc., 2023) Arsan, Taner; Guler,A.K.; Yildiz,E.; Kilinc,S.; Camlidere,B.; Arsan,T.
Network traffic anomaly detection describes a time series anomaly detection problem where a sudden increase or decrease (called spikes) in network traffic is predicted. Data is modeled with the trend and heteroscedastic noise component. Traditional autoregressive models struggle to capture data changes effectively, making anomaly detection difficult. Our approach is to generate upper and lower limits by using quantile regression. We use a deep learning based multilayer perceptron model to predict five data quantiles 1, 25, 50, 75, and 99. The upper and lower limits are calculated as differences between the quantile-1 and quantile-99. Any data that is outside these limits are considered as an anomaly. We also add tolerance to these limits to add flexibility to anomaly detection. Anomalies and non-anomalies are labeled to get a binary classification task. Anomaly detection is class imbalanced by nature; therefore, precision, recall, and F-1 score are computed to evaluate the proposed anomaly detection method. We conclude that choosing tolerance is a tradeoff between false alarms and missing anomaly detections. © 2023 IEEE.
Citation Count: 1
Physical Layer Security under the Effect of Dimming in Visible Light Communications;
(Institute of Electrical and Electronics Engineers Inc., 2023) Arsan, Taner; Panayırcı, Erdal; Gökhan,R.B.; Arsan,T.; Panayirci,E.
Visible Light Communication (VLC) is a promising option for 6G wireless systems to increase performance and provide a high-speed data rate compared to radio frequency (RF). Dimming control is one of the most important aspects to be considered in VLC. It is very critical for the future of VLC to adjust the brightness of the light source to be used in communication and to ensure that the light source, with a changing brightness, maintains the desired communication performance due to the situation and environment. This work proposes a novel pilot-aided estimation method for channel and dimming coefficients in spatial modulation (SM)-VLC systems. As a result, in the VLC system running with the PLS algorithm, while the information transmitted to the legal user is received with an excellent BER performance, it is observed that the BER performance of the eavesdropper is around 0.5, which is the worst possible situation for the illegal user. © 2023 IEEE.
Citation Count: 2
Power control and resource allocation in TDD-OFDM based femtocell networks with interference
(Institute of Electrical and Electronics Engineers Inc., 2018) Arsan, Taner; Panayırcı, Erdal; Panayirci,E.
Femtocell technology is a promising solution for different dilemmas in cellular networks. In femtocell power control, the interference experienced by the network is divided into two main tiers according to the type of network whose signal is interfering with another network. In utilizing the functionality of a two-tier network where femtocell technology is deployed, a major challenge is in sharing the frequency resource of a macrocell. This paper proposes an enhanced dynamic algorithm bounded by two constraints to optimize the transmission powers of femtocell users in TDD-OFDM based femtocell networks, taking into consideration rate enhancement of femtocell mobile stations. We compare our algorithm with the macrocell guard system, which allows femtocells to occupy only the subchannels unoccupied by the macrocell. © 2017 IEEE.
Citation Count: 0
Predictive Maintenance Analysis for Industries
(Institute of Electrical and Electronics Engineers Inc., 2024) Arsan, Taner; Arsan,T.
In this paper, we are focused on deriving conclusions from sensor parameter data that would enable the detection of potential faults and the prediction of failures. We used Random Forest, Decision Tree, Naive Bayes, Logistic Regression, Support Vector Machine, and Long Short-Term Memory models to predict faults for sensor data. This analysis, which predicts the failure, has been examined through the pump sensor dataset from Kaggle. It is a binary classification problem, and it performs time series analysis using historical pump sensor data to predict future observations and classify them into a positive label (normal) or a negative label (broken). The pump system must be in perfect condition to ensure continuous power supply. A failure of one of the pumps in the system can lead to a temporary drop in power generation and even a complete outage. This may be avoided if failures are predicted in advance. Therefore, it is important to anticipate failure early to avoid large financial losses. Predictive maintenance is beneficial for industries to prevent these faults and losses. Despite expectations, the Random Forest algorithm outperforms LSTM, followed by Decision Trees. Support Vector Machine and Naive Bayes algorithms show inferior performance compared to Random Forest and LSTM. © 2024 IEEE.
Citation Count: 0
A systems software architecture for training neural, fuzzy neural and genetic computational intelligent networks
(Institute of Electrical and Electronics Engineers Inc., 2006) Arsan, Taner; Öǧrenci,A.S.; Saydam,T.
A systems software architecture for training distributed neural, fuzzy neural and genetic networks and their relevant information models have been developed. Principles of on-line architecture building, training, managing, and optimization guidelines are provided and extensively discussed. Qualitative comparisons of neural training strategies have been provided. © 2006 IEEE.